31 citations · 64 across the 16 of their papers we have counts for
6 papers · 2 filters
Neural Fields in Robotics: A Survey
Muhammad Zubair Irshad, Mauro Comi, Yen-Chen Lin +5
Neural Fields have emerged as a transformative approach for 3D scene representation in computer vision and robotics, enabling accurate inference of geometry, 3D semantics, and dyna…
Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching
Eugenio Chisari, Nick Heppert, Max Argus +3
Learning from expert demonstrations is a promising approach for training robotic manipulation policies from limited data. However, imitation learning algorithms require a number of…
Imagine2touch: Predictive Tactile Sensing for Robotic Manipulation using Efficient Low-Dimensional Signals
Abdallah Ayad, Adrian Röfer, Nick Heppert +1
Humans seemingly incorporate potential touch signals in their perception. Our goal is to equip robots with a similar capability, which we term Imagine2touch. Imagine2touch aims to…
CenterArt: Joint Shape Reconstruction and 6-DoF Grasp Estimation of Articulated Objects
Sassan Mokhtar, Eugenio Chisari, Nick Heppert +1
Precisely grasping and reconstructing articulated objects is key to enabling general robotic manipulation. In this paper, we propose CenterArt, a novel approach for simultaneous 3D…
DITTO: Demonstration Imitation by Trajectory Transformation
Nick Heppert, Max Argus, Tim Welschehold +2
Teaching robots new skills quickly and conveniently is crucial for the broader adoption of robotic systems. In this work, we address the problem of one-shot imitation from a single…
PseudoTouch: Efficiently Imaging the Surface Feel of Objects for Robotic Manipulation
Adrian Röfer, Nick Heppert, Abdallah Ayad +2
Tactile sensing is vital for human dexterous manipulation, however, it has not been widely used in robotics. Compact, low-cost sensing platforms can facilitate a change, but unlike…